Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets

Cited by (433)

Items 101–200 of 433

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2. MOLECULAR SIMILARITY ANALYSIS
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5. Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity
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6. Bimolecular Nucleophilic Substitution Reactions: Predictive Models for Rate Constants and Molecular Reaction Pairs Analysis
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10. A Data Mining Method To Facilitate SAR Transfer
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11. Meta Learning for Low-Resource Molecular Optimization
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12. Activity profiles of analog series containing pan assay interference compounds
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14. A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions
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16. Systemic evolutionary chemical space exploration for drug discovery
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18. Fluorination patterns in small alkyl groups
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19. The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics
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20. The influence of hydrogen bonding on partition coefficients
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22. Monitoring the Progression of Structure–Activity Relationship Information during Lead Optimization
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23. Do Medicinal Chemists Learn from Activity Cliffs? A Systematic Evaluation of Cliff Progression in Evolving Compound Data Sets
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24. Integrating the Structure–Activity Relationship Matrix Method with Molecular Grid Maps and Activity Landscape Models for Medicinal Chemistry Applications
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25. Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods
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27. Discovery of New Inhibitors of Transforming Growth Factor-Beta Type 1 Receptor by Utilizing Docking and Structure-Activity Relationship Analysis
    Junhao Jiang, Ping Deng · 2019 · International Journal of Molecular Sciences
28. A medicinal chemistry perspective on melting point: matched molecular pair analysis of the effects of simple descriptors on the melting point of drug-like compounds
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30. Advancing the activity cliff concept, part II
    Dagmar Stumpfe, Antonio de la Vega de León, Dilyana Dimova, Jürgen Bajorath · 2014 · F1000Research
31. Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures
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32. Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization
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33. Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches
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34. DeepAC – conditional transformer-based chemical language model for the prediction of activity cliffs formed by bioactive compounds
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35. Exploring the Scaffold Universe of Kinase Inhibitors
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36. In Silico ADME Techniques Used in Early-Phase Drug Discovery
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37. Novel compounds targeting InhA for TB therapy
    Manaf AlMatar, Essam A. Makky, İşıl Var, Begüm Kayar, Fatih Köksal · 2017 · Pharmacological Reports
38. Progress with modeling activity landscapes in drug discovery
    Martin Vogt · 2018 · Expert Opinion on Drug Discovery
39. Duality of activity cliffs in drug discovery
    Jürgen Bajorath · 2019 · Expert Opinion on Drug Discovery
40. SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design
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41. Identification of Tyrosinase Inhibitors and Their Structure-Activity Relationships via Evolutionary Chemical Binding Similarity and Structure-Based Methods
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42. Matched Molecular Series Analysis for ADME Property Prediction
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43. Computational drug design in the artificial intelligence era: A systematic review of molecular representations, generative architectures, and performance assessment
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44. Discovery of InhA inhibitors with anti-mycobacterial activity through a matched molecular pair approach
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45. A fingerprint pair analysis of hERG inhibition data
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46. Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure
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47. Identifying Promiscuous Compounds with Activity against Different Target Classes
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48. Deep understanding of structure–solubility relationship for a diverse set of organic compounds using matched molecular pairs
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49. Comprehensive Analysis of Three-Dimensional Activity Cliffs Formed by Kinase Inhibitors with Different Binding Modes and Cliff Mapping of Structural Analogues
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50. Prediction of activity cliffs on the basis of images using convolutional neural networks
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51. Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design
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52. Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression
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53. The Playbooks of Medicinal Chemistry Design Moves
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54. “Molecular Anatomy”: a new multi-dimensional hierarchical scaffold analysis tool
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55. Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity
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56. Novel pyrazolo[1,5-a]pyridines as orally active EP1 receptor antagonists: Synthesis, structure-activity relationship studies, and biological evaluation
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57. X-ray-Structure-Based Identification of Compounds with Activity against Targets from Different Families and Generation of Templates for Multitarget Ligand Design
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58. Neighborhood-Based Prediction of Novel Active Compounds from SAR Matrices
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59. De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder
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60. Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome
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61. Chemical Transformations That Yield Compounds with Distinct Activity Profiles
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62. QSAR-assisted-MMPA to expand chemical transformation space for lead optimization
    Li Fu, Ziyi Yang, Zhijiang Yang, Mingzhu Yin, Aiping Lü, Xiang Chen, Shao Liu, Tingjun Hou, Dongsheng Cao · 2020 · Briefings in Bioinformatics
63. Structural Effects on the Antioxidant Properties of Amino Acid Betaxanthins
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64. Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets
    Huabin Hu, Dagmar Stumpfe, Jürgen Bajorath · 2018 · ACS Omega
65. Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods
    Chaofeng Lou, Hongbin Yang, Hua Deng, Mengting Huang, Weihua Li, Guixia Liu, Philip W. Lee, Yun Tang · 2023 · Journal of Cheminformatics
66. QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors
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67. Prediction of Individual Compounds Forming Activity Cliffs Using Emerging Chemical Patterns
    Vigneshwaran Namasivayam, Preeti Iyer, Jürgen Bajorath · 2013 · Journal of Chemical Information and Modeling
68. Mining the Cambridge Structural Database for Matched Molecular Crystal Structures: A Systematic Exploration of Isostructurality
    Ilenia Giangreco, Jason C. Cole, Elizabeth Thomas · 2017 · Crystal Growth & Design
69. Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures
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70. Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions
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71. Nonadditivity in public and inhouse data: implications for drug design
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72. Identification of target family directed bioisosteric replacements
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73. Method for the Evaluation of Structure–Activity Relationship Information Associated with Coordinated Activity Cliffs
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74. Applicability Domains and Consistent Structure Generation
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75. Application of deep generative model for design of Pyrrolo[2,3-d] pyrimidine derivatives as new selective TANK binding kinase 1 (TBK1) inhibitors
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77. SAR mining and its application to the design of TRPA1 antagonists
    James E. J. Mills, Alan D. Brown, Thomas Ryckmans, Duncan C. Miller, Sarah E. Skerratt, Carolyn M. Barker, Mark E. Bunnage · 2011 · MedChemComm
78. Mapping of inhibitors and activity data to the human kinome and exploring promiscuity from a ligand and target perspective
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79. Exploring Activity Cliffs from a Chemoinformatics Perspective
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80. Improving the Utility of Molecular Scaffolds for Medicinal and Computational Chemistry
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81. Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications
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